Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation

Adams, Jason Y.; Lieng, Monica K.; Kühn, Bärbel; Rehm, Gregory B.; Guo, Edward C.; Taylor, Sandra L.; Delplanque, Jean‐Pierre; Anderson, Nick

doi:10.1038/s41598-017-15052-x

Cited by 25 publications

(19 citation statements)

References 41 publications

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“…Peak pressure or the difference between set and observed tidal volume may serve as reasonable surrogates for these variables but warrant further study. Direct analysis of the ventilator waveform data could also provide even more detail of ventilator management if such data is available (32).…”

Section: Discussionmentioning

confidence: 99%

Evaluating Delivery of Low Tidal Volume Ventilation in Six ICUs Using Electronic Health Record Data*

Sjoding

Gong

Haas

et al. 2019

Critical Care Medicine

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Objective: Mechanical ventilation with low tidal volumes is recommended for all patients with ARDS, and may be beneficial to other intubated patients, yet consistent implementation remains difficult to obtain. Using detailed electronic health record data, we examined patterns of tidal volume administration, the effect on clinical outcomes, and alternate metrics for evaluating low tidal volume compliance in clinical practice. Design: Observational cohort study. Setting: Six intensive care units in a single hospital system. Patients: Adult patients who received invasive mechanical ventilation more than 12 hours. Intervention: none Measurements and Main Results: Tidal volumes were analyzed across 1,905 hospitalizations. Although mean tidal volume was 6.8 mL/kg predicted body weight (PBW), 40% of patients were exposed to tidal volumes > 8 mL/kg PBW, with 11% for more than 24 hours. At a patient level, exposure to 24 total hours of tidal volumes > 8 mL/kg PBW was associated with increased mortality (odds ratio = 1.82, 95% CI 1.20 – 2.78), while mean tidal volume exposure was not (odds ratio = 0.87 per 1 mL/kg increase, 95% CI 0.74 – 1.02). Initial tidal volume settings strongly predicted exposure to volumes > 8 mL/kg for 24 hours, the adjusted rate was 21.5% when initial volumes were > 8 mL/kg PBW and 7.1% when initial volumes were < 8 mL/kg PBW. Across ICUs, correlation of mean tidal volume with alternative measures of low tidal volume delivery ranged from 0.38 to 0.66. Conclusion: Despite low mean tidal volume in the cohort, a significant percentage of patients were exposed to a prolonged duration of high tidal volumes which was correlated with higher mortality. Detailed ventilator records in the electronic health record provide a unique window for evaluating low tidal volume delivery and targets for improvement.

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Section: Discussionmentioning

confidence: 99%

Evaluating Delivery of Low Tidal Volume Ventilation in Six ICUs Using Electronic Health Record Data*

Sjoding

Gong

Haas

et al. 2019

Critical Care Medicine

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“…Ventiliser uses Python, a popular computer language and its freely available data science libraries, allowing us to make it freely available to clinicians and researchers. The use of Python also enables the possibility for interfacing with existing open source platforms such as ventMap 10 and installation on devices such as the RaspberryPi to enable real-time distributed processing and data collection at the bedside. We used a rule-based rather than a machine learning algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several different approaches have been reported to computationally analyse adult ventilator data and characterise patient-ventilator interactions. [10][11][12][13][14][15][16][17] However, neonatal mechanical ventilation uses different ventilator modes and has different characteristics from adult ventilation and the tools developed for adults cannot be directly used with neonatal data.…”

Section: Introductionmentioning

confidence: 99%

Computational analysis of neonatal ventilator waveforms and loops

2020

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Background Modern neonatal ventilators allow the downloading of their data with a high sampling rate. We wanted to develop an algorithm that automatically recognises and characterises ventilator inflations from ventilator pressure and flow data. Methods We downloaded airway pressure and flow data with 100 Hz sampling rate from Dräger Babylog VN500 ventilators ventilating critically ill infants. We developed an open source Python package, Ventiliser , that includes a rule-based algorithm to automatically discretise ventilator data into a sequence of flow and pressure states and to recognise ventilator inflations and an information gain approach to identify inflation phases (inspiration, expiration) and sub-phases (pressure rise, pressure plateau, inspiratory hold etc.). Results Ventiliser runs on a personal computer and analyses 24 h of ventilation in 2 min. With longer recordings, the processing time increases linearly. It generates a table reporting indices of each breath and its sub-phases. Ventiliser also allows visualisation of individual inflations as waveforms or loops. Ventiliser identified >97% of ventilator inflations and their sub-phases in an out-of-sample validation of manually annotated data. We also present detailed quantitative analysis and comparison of two 1-hour-long ventilation periods. Conclusions Ventiliser can analyse ventilation patterns and ventilator–patient interactions over long periods of mechanical ventilation. Impact We have developed a computational method to recognize and analyse ventilator inflations from raw data downloaded from ventilators of preterm and critically ill infants. There have been no previous reports on the computational analysis of neonatal ventilator data. We have made our program, Ventiliser , freely available. Clinicians and researchers can use Ventiliser to analyse ventilator inflations, waveforms and loops over long periods. Ventiliser can also be used to study ventilator–patient interactions.

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“…Rhem et al [40] and Adams et al [41] developed a set of algorithms to detect two types of asynchrony associated with dynamic hyperinflation, double triggering, and flow asynchrony. Based on a learning database of 5075 breaths from 16 patients, they developed logical operators to recognize double triggering based on bedside clinical rules.…”

Section: Mechanical Ventilationmentioning

confidence: 99%